Andriy I. Bandos

Background/Objectives: Circumscribed oval, hypoechoic masses are common on screening breast ultrasound (US), and the vast majority are benign. Triple-receptor negative invasive breast cancer (TNBC) can appear similar, resulting in both human and artificial intelligence (AI) interpretive errors. Purpose: We sought to improve AI performance in distinguishing common benign masses from TNBC through a retrospective model refinement and validation study. Materials and Methods: In an Institutional Review Board-approved HIPAA-compliant protocol, from five academic medical centers, orthogonal ultrasound images of 1771 breast masses 2 cm or smaller were acquired, consisting of cysts, complicated cysts, other benign, and malignancies. Cases were randomized, controlling for lesion class, site, and patient age, with 1446 (including 402, 27.8%, malignancies) used for training and 325 (including 95, 29.2% malignancies) for validation using Koios DS^® (decision support, KDS) software version 2.0. A breast imaging radiologist from each center reviewed images and recorded BI-RADS features and assessment. Demographics, symptoms, and pathology or at least one-year follow-up was recorded. The KDS score was evaluated standalone and in combination with BI-RADS using logistic regression and ROC analysis with focus on specificity at sensitivity of 98%. Results: In training, KDS standalone performed comparably to BI-RADS, and significantly improved BI-RADS malignancy risk prediction (p < 0.001). The 98%–sensitivity threshold for combined KDS + BI-RADS was estimated and kept fixed during validation. In validation, KDS standalone performed similar to BI-RADS with AUC = 0.97 (CI: 0.95–0.98) versus 0.95 (p = 0.22), with sensitivity of 98% (93/95, CI: 95–100%) for both and specificity of 70.9% (163/230, CI: 65.0–76.7%) for KDS versus 63.9% for BI-RADS (147/230, p = 0.10). Combining KDS + BIRADS significantly improved overall performance (AUC 0.98, p < 0.001) and specificity (74.4%, 171/230, p < 0.001) while maintaining sensitivity at 98% (93/95). Conclusions: While KDS alone should not replace BI-RADS, when used in combination with BI-RADS, it can significantly improve specificity for highly accurate (98% sensitivity) triaging management of masses representative of those seen on screening US.

Among women eligible for supplemental MRI screening according to state insurance law, adding contrast-enhanced mammography to digital breast tomosynthesis significantly increased early breast cancer detection alongside a 13% increase in the false-positive rate.

Observational cross-sectional data from a large academic screening practice show significant changes in false-positive rates and interpretation times during batch reading of digital breast tomosynthesis images.

In this prospective trial interim report, adding contrast-enhanced mammography to digital breast tomosynthesis increased breast cancer detection with limited increase in recalls in women with personal history of breast cancer.

PURPOSE: In the United States, a comprehensive national breast cancer registry (CR) does not exist. Thus, care and coverage decisions are based on data from population subsets, other countries, or models. We report a prototype real-world research data mart to assess mortality, morbidity, and costs for breast cancer diagnosis and treatment. METHODS: With institutional review board approval and Health Insurance Portability and Accountability Act (HIPPA) compliance, a multidisciplinary clinical and research data warehouse (RDW) expert group curated demographic, risk, imaging, pathology, treatment, and outcome data from the electronic health records (EHR), radiology (RIS), and CR for patients having breast imaging and/or a diagnosis of breast cancer in our institution from January 1, 2004, to December 31, 2020. Domains were defined by prebuilt views to extract data denormalized according to requirements from the existing RDW using an export, transform, load pattern. Data dictionaries were included. Structured query language was used for data cleaning. RESULTS: Five-hundred eighty-nine elements (EHR 311, RIS 211, and CR 67) were mapped to 27 domains; all, except one containing CR elements, had cancer and noncancer cohort views, resulting in a total of 53 views (average 12 elements/view; range, 4-67). EHR and RIS queries returned 497,218 patients with 2,967,364 imaging examinations and associated visit details. Cancer biology, treatment, and outcome details for 15,619 breast cancer cases were imported from the CR of our primary breast care facility for this prototype mart. CONCLUSION: Institutional real-world data marts enable comprehensive understanding of care outcomes within an organization. As clinical data sources become increasingly structured, such marts may be an important source for future interinstitution analysis and potentially an opportunity to create robust real-world results that could be used to support evidence-based national policy and care decisions for breast cancer.

PurposeRadiologists and other image readers spend prolonged periods inspecting medical images. The visual system can rapidly adapt or adjust sensitivity to the images that an observer is currently viewing, and previous studies have demonstrated that this can lead to pronounced changes in the perception of mammogram images. We compared these adaptation effects for images from different imaging modalities to explore both general and modality-specific consequences of adaptation in medical image perception.ApproachWe measured perceptual changes induced by adaptation to images acquired by digital mammography (DM) or digital breast tomosynthesis (DBT), which have both similar and distinct textural properties. Participants (nonradiologists) adapted to images from the same patient acquired from each modality or for different patients with American College of Radiology—Breast Imaging Reporting and Data System (BI-RADS) classification of dense or fatty tissue. The participants then judged the appearance of composite images formed by blending the two adapting images (i.e., DM versus DBT or dense versus fatty in each modality).ResultsAdaptation to either modality produced similar significant shifts in the perception of dense and fatty textures, reducing the salience of the adapted component in the test images. In side-by-side judgments, a modality-specific adaptation effect was not observed. However, when the images were directly fixated during adaptation and testing, so that the textural differences between the modalities were more visible, significantly different changes in the sensitivity to the noise in the images were observed.ConclusionsThese results confirm that observers can readily adapt to the visual properties or spatial textures of medical images in ways that can bias their perception of the images, and that adaptation can also be selective for the distinctive visual features of images acquired by different modalities.

BACKGROUND AND PURPOSE: A high-intensity zone identified on preprocedural MR imaging is known to correlate with pain at provocation lumbar discography. The correlation between enhancing annular fissures and pain at provocation lumbar discography has not been comprehensively evaluated. The purpose of this study was to assess the pain response and imaging features at enhancing annular fissure nonoperated disc levels identified on preprocedural MR imaging with comparison with the high-intensity zone and nonenhancing disc levels in patients referred for provocation lumbar discography. MATERIALS AND METHODS: One-hundred nonoperated discs in 44 patients were retrospectively evaluated for an enhancing annular fissure on sagittal postcontrast T1-weighted pre-discogram MR imaging. Enhancing annular fissure discs were graded on the sagittal T2-weighted sequence (Grade 4: like CSF to Grade 1: negative/barely visible) for high-intensity-zone conspicuity. High-intensity-zone detection was performed independently. In the primary assessment, enhancing annular fissure and high-intensity zones were associated with pain response at provocation lumbar discography. Additional analysis included intradiscal anesthetic response and postdiscogram CT appearance. RESULTS: Thirty-nine discs demonstrated an enhancing annular fissure, with 23/39 demonstrating a high-intensity zone. The presence of a high-intensity zone predicted severe pain (concordant + nonconcordant; P = .005, sensitivity of 40%, specificity of 94%) and concordant pain (P = .007, sensitivity of 39%, specificity of 86%) at provocation lumbar discography. Enhancing annular fissures without a detected high-intensity zone were more frequently observed among severely painful (50%) and concordant (36%) discs than among discs negative for pain (9%; P = .01). This finding resulted in a substantially greater overall sensitivity of enhancing annular fissures for severe (P < .001, 64%) and concordant pain (P = .008, 61%), significantly improving the overall predictive ability of a high-intensity zone alone. A high-intensity zone went undetected in 9/11 Grade 1 disc levels with concordant pain present in 7/9. CONCLUSIONS: Consideration of enhancing annular fissures on preprocedural MR imaging substantially improves the prediction of severe/concordant pain in provocation lumbar discography.

Objective To define the uniqueness of chest CT infiltrative features associated with COVID-19 image characteristics as potential diagnostic biomarkers. Methods We retrospectively collected chest CT exams including n = 498 on 151 unique patients RT-PCR positive for COVID-19 and n = 497 unique patients with community-acquired pneumonia (CAP). Both COVID-19 and CAP image sets were partitioned into three groups for training, validation, and testing respectively. In an attempt to discriminate COVID-19 from CAP, we developed several classifiers based on three-dimensional (3D) convolutional neural networks (CNNs). We also asked two experienced radiologists to visually interpret the testing set and discriminate COVID-19 from CAP. The classification performance of the computer algorithms and the radiologists was assessed using the receiver operating characteristic (ROC) analysis, and the nonparametric approaches with multiplicity adjustments when necessary. Results One of the considered models showed non-trivial, but moderate diagnostic ability overall (AUC of 0.70 with 99% CI 0.56–0.85). This model allowed for the identification of 8–50% of CAP patients with only 2% of COVID-19 patients. Conclusions Professional or automated interpretation of CT exams has a moderately low ability to distinguish between COVID-19 and CAP cases. However, the automated image analysis is promising for targeted decision-making due to being able to accurately identify a sizable subsect of non-COVID-19 cases. Key Points • Both human experts and artificial intelligent models were used to classify the CT scans. • ROC analysis and the nonparametric approaches were used to analyze the performance of the radiologists and computer algorithms. • Unique image features or patterns may not exist for reliably distinguishing all COVID-19 from CAP; however, there may be imaging markers that can identify a sizable subset of non-COVID-19 cases.